Promoting active participation in robot-aided rehabilitation via machine learning and impedance control.

Introduction: Active patient participation is crucial for effective robot-assisted rehabilitation. Quantifying the user's Active Level of Participation (ALP) during therapy and developing human-robot interaction strategies that promote engagement can improve rehabilitation outcomes. However, existing methods for estimating participation are often unimodal and do not provide continuous participation assessment.

Comparison of Point Cloud Registration Techniques on Scanned Physical Objects.

This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds generated from the Cranfield benchmark. Point clouds are sampled from existing CAD models and 3D scans of physical objects, introducing real-world complexities such as noise and outliers.

Transparent Interaction Based Learning for Human-Robot Collaboration.

The number of collaborative robots that perform different tasks in close proximity to humans is increasing. Previous studies showed that enabling non-expert users to program a cobot reduces the cost of robot maintenance and reprogramming. Since this approach is based on an interaction between the cobot and human partners, in this study, we investigate whether making this interaction more transparent can improve the interaction and lead to better performance for non-expert users.